Method and apparatus for multi-user detection using RFSQ successive interference cancellation in CDMA wireless systems

ABSTRACT

A method and apparatus for using a multi-user detector for reducing multiple access interference (MAI) in a direct sequence CDMA wireless system such as W-CDMA. A superconducting rapid single flux quantum (RSFQ) RF digital receiver operating in combination with an RSFQ successive interference canceller (SIC) is located in the base stations of the wireless system. In the present invention, the RSFQ SIC is a vector machine capable of processing the cross-correlation matrices using an iterative method to decorrelate the user binary code sequences from the input signal in which the interference components are removed. According, the reduction in interference results in a significant increase in system capacity while improving cellular coverage area.

FIELD OF THE INVENTION

[0001] The present invention relates generally to interference reductionin wireless telecommunication systems and, more particularly, tointerference cancellation for reducing interference in CDMA basedsystems.

BACKGROUND OF THE INVENTION

[0002] Wireless telecommunication service providers are keenlyinterested in providing high quality, reliable services for theircustomers in today's highly competitive marketplace. A significantaspect affecting the service quality is the consistency of radiocoverage within cell coverage areas of the network. Moreover, anadditional aim from the provider's perspective is to be able to increasecapacity while maintaining quality and reliability. As known by thoseskilled in the art, telecommunication networks operating in accordancewith direct sequence code division multiple access (DS-CDMA), which arealso referred to as spread spectrum systems, the service quality isparticularly affected by the number of users in the cell. This isbecause the inherent nature of spread spectrum systems permits all usersto transmit and receive on a common frequency band thus each of thetransmissions necessarily “interfere” with each other in what is knownas multiple access interference (MAI).

[0003] As the number of users in the cell increase more interference isintroduced causing the mobiles to transmit with increased power in orderto sufficiently communicate with the base station and thereby making theproblem worse. This effect tends to be more prominent on uplinktransmissions from mobiles since their power levels tend to be morelimited in comparison to that of the base station. Another consequenceof increased interference is that the cell coverage area tends tocontract, on the other hand as less traffic is present, the coveragearea of the cell tends to expand. The tendency for cells to shrink andexpand in relation to number of users in the cell is known as “cellbreathing” and occurs, for example, since each user in a CDMA systemcumulatively contributes to the interference in the cell since theysimultaneously share a common frequency band.

[0004] Another type of interference that has a significant affect onCDMA systems is multipath interference. Radio channel signals between atransmitter and receiver typically do not propagate only along one path.Reflections and refractions of a signal, which are particularly acute inurban environments having many buildings and obstructions, will bereceived over a number of different paths which are copies of thetransmitted signal, each having different amplitudes, phases, temporaldelays and arrival angles. At the receiver these signals can interferewith each other, in some instances being constructive at some points anddestructive at others. Still another type of interference is theso-called near-far interference which happens when a strong signal froma mobile close to the base station overwhelms a weaker signal from adistant mobile closer to the cell boundary, for example.

[0005] An advantage of CDMA systems is that spread spectrum modulationcombined with the use of a Rake receiver, can be effective againstmultipath interference. Rake receivers are used to receive and resolvemultipath signals in which multiple copies of the signal are receivedwith varying delays. This is because the spread spectrum waveform iswell matched to the multipath channel and thus CDMA signals employ theuse of multipath diversity, which also reduces the effect from signalfading.

[0006]FIG. 1 shows a simplified block diagram of an exemplary directsequence CDMA transmitter. The binary data signal is directly modulatedby a discrete code valued signal that is discrete in time. The datasignal is multiplied by the code signal shown by the code generatorblock 110 whereby the resulting signal modulates the wideband carriershown by the wideband modulator block 120. A carrier generator 130 thenmodulates the wideband carrier signal 120 for transmission throughantenna 140. Various code modulation techniques can be used. Often theseare a form of phase shift keying such as binary phase shift keying(BPSK) or quadrature phase shift keying (QPSK), for example. The codesignal is referred to as the chip rate in which one chip is equal to onesymbol in the spreading code signal.

[0007]FIG. 2 shows a simplified block diagram of an exemplary directsequence CDMA receiver. At the receiver, the signal is first downconverted with the help of carrier generator 250. The signal is thencoherently detected and filtered with the help of filter matching on thechip waveform that is then despread with despreader block 210. In orderto despread the signals, the receiver must know the code sequence usedin spreading the signal, the codes of the received signal, together withthe condition that the locally generated code must be synchronized. Thecode synchronization tracking block 230 performs the operation ofsynchronizing the signal during the entire time the signal is beingreceived. The signal despread by dispreading block 210 is fed into datademodulator block 240. The data demodulator block 240 demodulates thesignal in order to allow the original data to be recovered. Recovery ofthe data is possible at the receiving end since the code sequences areknown a priori.

[0008] The lack of spectral resources coupled with increasing demand forwireless voice and data services such as Internet access, audio, video,and multimedia applications have highlighted capacity as a criticalissue. Among other things the lack of capacity has been the drivingforce behind the shift to wideband systems such as Wideband CodeDivision Multiple Access (W-CDMA). W-CDMA standard is a preferred airinterface fulfilling 3G requirements for improved quality of service andcapacity. It is able to provide connections of 384 bits/s for mobileapplications and as much as 2 Mbits/s in stationary environments. Thecapacity of direct sequence DS-CDMA systems such as W-CDMA using a Rakereceiver is interference limited i.e. more users in a cell creates moreinterference for all the other users. Since the spreading codes insignals from the users are not completely orthogonal, this results inresidual interference within the cell, known as multiple accessinterference (MAI), where when together with interference fromneighboring cells, significantly degrades performance in the cell. MAIis a major factor in limiting cell capacity and the removal of suchinterference would lead to a significant increase in capacity.

[0009] A conventional approach for dealing with interference is byemploying a single-user matched filter in combination with Rakecombiner. The users use spreading sequences of nearly uncorrelated codesso that interference from other users are treated as non-coherentinterference and rejected, however this technique has been shown not tobe the optimal approach. This is because the sum of thecross-correlation between codes at high loading can be significantlylarger than the autocorrelation that is detected. Furthermore, theinterference itself contains much information on the structure andcontent of the signal and can be used advantageously. In W-CDMA,multi-user detectors (MUD), also referred to as interference cancellers,provide a means for reducing the effect of multiple access interference.A multi-user detector (MUD) is an advanced detector in base stationsthat uses a more sophisticated approach to remove interferencecomponents from the signal. The benefit of using MUDs is that theydramatically increase system capacity. Furthermore, they can be usedeffectively for mitigating the effect of near-far interference thattypically can plague DS-CDMA systems, by first detecting, and thensubtracting the problem mobile from the input signal.

[0010] However, use of the optimum multi-user detector has not beenfound to be practical for implementation. This is because the complexityof the optimum detector becomes exponential to the number of users andrequires computations that are too demanding for the current siliconbased IC processing technology or any conventional digital technologycurrently employed. Thus, a number of suboptimum multi-user andinterference receivers have been proposed or developed. The suboptimumreceivers can be classified into two major categories: linear multi-userdetectors and subtractive interference cancellers. Linear detectorsapply a linear transform into the outputs of the matched filters thattry to remove the MAI i.e. interference resulting from correlationsbetween user codes. Examples of linear multi-user detectors that aremost commonly referred to are decorrelating detectors, where the linearfilter has a zero output, and the minimum mean square error (LMMSE)detector, where the linear filter has a minimum output energy. Insubtractive interference cancellation, the MAI is estimated and thensubtracted from the received signal. The cancellation can be performedwith successive interference cancellation (SIC) or with parallelinterference cancellation (PIC).

[0011] The successive cancellation technique requires at least severaliterations for a user however the individual iterations are less complexthan with the parallel cancellation technique. Furthermore, SIC does notdistinguish users from one another from the spreading sequences and thecanceling is performed serially in which the delay bits are added suchthat the complexity increases linearly with the number of users anditerations. Moreover, SIC has less computational complexity than PIC,which is more hardware intensive to process users in parallel. In acomparative sense, PIC is based on a Jacobi algorithm and thus requiresspecific conditions on the interference matrix for convergence. Specialtechniques can be used to reach convergence for particular scenario ofmobile transmission but there is generally no unique solution. Moreover,PIC typically requires more iterations than SIC, which means theconverge rate is usually much slower. The computational complexity isconsiderably larger in PIC due to the intensive parallel processing thathas to be applied. In SIC, the converge rate is generally much fasterand the processing flow can be serialized.

[0012] Even though the relative complexity is lower, the computationaldemands from SIC require processor circuits capable of producing severalGiga operations per second, which is extremely challenging when usingconventional integrated circuits. The serial nature of the SIC algorithmrequires very fast electronics in order to process the many channelspresent in the multiple access interference. Furthermore, when assuminglong spreading codes, the cross-correlation between the spreading codeschanges from symbol to symbol instead of over several symbols, therebyincreasing the complexity and required processing even further. In fact,the reason for the option of using short spreading codes in W-CDMA wasto enable the future use of a multi-user detector with the projectedavailable processing power. However, the use of long codes is preferabledue to better interference averaging, and thus better performance incanceling the interference. The implementation of comprehensive SICalgorithms are currently not practical due to conventional hardwarelimitations and, at present, only partial cancellation methods have beenconsidered, which have not been found to be very beneficial.

[0013] In view of the foregoing, it is desirable to provide atechnically viable solution for using interference cancellation inwireless CDMA systems.

SUMMARY OF THE INVENTION

[0014] Briefly described and in accordance with embodiments and featuresthereof, the invention provides a method and apparatus for implementinga multi-user detector for reducing multiple access interference (MAI) indirect sequence CDMA wireless systems such as W-CDMA. In accordance witha first architecture embodiment of the present invention, adecorrelator-Rake arrangement is implemented where an input signal,received by the base station, comprising a plurality user of partiallycross-correlated binary code sequences is fed into a bank of matchedfilters (despreaders) as a first stage of interference removal. Theoutput vector from the bank of matched filters is fed into asuperconducting rapid single flux quantum (RSFQ) vector processing unitapplying a successive interference cancellation (SIC) Gauss-Seideliterative algorithm to the cross-correlation matrices to decorrelate theuser binary code sequences. The interference components are removed andthe output from the RSFQ SIC is fed into a bank of Rake combiners torecover original data transmitted by the mobile users.

[0015] In a second architecture embodiment of the invention, aRake-decorrelator arrangement is implemented where the input signalcomprising a plurality of user binary code sequences is fed into a bankof matched filters. The output vector from the matched filters is fedinto a bank of Rake combiners. The output vector from the Rake combinersis fed into the RSFQ vector processing unit which applies a SICGauss-Seidel iterative algorithm to the cross-correlation matrices todecorrelate the user binary code sequences. The interference componentsare then removed to recover original data transmitted by the mobileusers.

[0016] In another aspect of the invention, within the RSFQ SIC the inneriterations, performed inside the main iterations dealing with signalsarrived at different time intervals, can be also performed in a parallelway using an Jacobi algorithm, for example.

[0017] In further aspect of the invention, the signal processingrequired for multi-user detection is performed before the matchedfilters. Further included is a memory for the storing signal processingdata such that the size of the memory is in proportion to the totalnumber of transmitted signals per packet multiplied by the oversamplingfactor.

[0018] The invention, through which the reduction in interference isachieved, results in at least a two-fold increase in uplink capacity ofthe W-CDMA system while improving cellular coverage. Furthermore, themulti-user detector of the invention improves power management and leadsto the lowering of radiated power of approximately 4-10 dB whileincreasing the average data transmission rate with less retransmissionsdue to bit errors. Additionally, the multi-user detector can beinstalled into base stations as needed since it can be readily connectedto preexisting outputs of the matched filters or Rake combiners. Theadvantages will be more clearly understood when taken together with thefollowing detailed description.

BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The invention, together with further objectives and advantagesthereof, may best be understood by reference to the followingdescription taken in conjunction with the accompanying drawings inwhich:

[0020]FIG. 1 shows a block diagram of an exemplary direct sequence CDMAtransmitter;

[0021]FIG. 2 shows a block diagram of an exemplary direct sequence CDMAreceiver;

[0022]FIG. 3 shows a block diagram for a first embodiment architectureof the RSFQ SIC implemented into the digital signal processing chain ofa base station receiver operating in accordance with the invention;

[0023]FIG. 4 shows a block diagram for a second embodiment architectureof the RSFQ SIC implementation operating in accordance with theinvention;

[0024]FIG. 5 shows a block diagram of a two-channel RSFQcross-correlation combiner operating in accordance with the invention;

[0025]FIG. 6 shows a simplified block diagram of an exemplary RFSQ MACoperating in accordance with the invention; and

[0026]FIG. 7 is a general block diagram of the RSFQ SIC multiprocessorarchitecture operating in accordance with the invention.

DETAILED DESCRIPTION OF THE INVENTION

[0027] The present invention is directed toward improving the capacityand spectral efficiency in direct sequence CDMA systems, such as W-CDMA,by reducing the multiple access interference (MAI) generated by userswithin the cell and by neighboring cells. The presence of MAI can beattributed to the non-perfect separation of the users in the despreadingprocess. A practical implementation of a multi-user detector (MUD) inwireless base stations is carried out by overcoming the processingdeficiencies of prior art MUDs described in the preceding sections. Inaccordance with the present invention, the computational processing ofthe multi-user detector using long spreading codes is performed bysuperconducting digital rapid single flux quantum (RSFQ) devices.Further described by the present invention is the general idea ofmulti-user detection and various embodiment architectures that areimplemented in RSFQ technology.

[0028] The field of superconductor electronics has made remarkableadvances over the last 25 years. The commercial viability ofsuperconductor technology has been demonstrated by the success and usein products such as MRI medical scanning machines for many years. Asknown by those skilled in the art, digital electronics based on rapidsingle flux quantum (RSFQ) has been shown to provide extraordinaryperformance characteristics of ultra-high speed and extremely low powerconsumption. The storage information in RSFQ devices is based on thefundamental phenomenon of quantization of magnetic flux and implementedin Josephson junctions. Ultra-high speed RSFQ logic circuits have beendeveloped that can operate in excess of 100 GHz. RSFQ ICs can befabricated from several superconducting metals such as niobium (Nb/NbN)operating at around 4-10 K (degrees Kelvin) or many of the so-calledhigh-temperature superconductors (HTS) operating between 20-77 K.Ultra-high speed superconductor logic circuits of this type areavailable e.g. from HYPRES Incorporated of Elmsford, N.Y., U.S.A.

[0029] While these temperatures are extremely low, there arerefrigerators, also referred known as cryocoolers, presently on themarket that can sustain these temperatures that are very reliable andeconomical with service lifetimes on the order of 15 years. Cryocoolersexist that have been developed specifically for use in wireless basestations that fit into a standard 19-inch (28 cm) rack. Cryocoolers ofthis type are sold by e.g. Conductus Incorporated of Sunnyvale, Calif.,U.S.A. and Superconductor Technologies Incorporated of Santa Barbara,Calif., U.S.A.

[0030] A description follows of two exemplary architecture embodimentsused in the implementation of the multi-user detector of the presentinvention.

[0031]FIG. 3 shows a block diagram for a first embodiment architectureof the RSFQ SIC implemented into the digital signal processing chain ofa W-CDMA base station receiver operating in accordance with theinvention. The RSFQ SIC operates with data coming either from adespreader or matched filters 310. Inserted in this way into the signalprocessing chain the RSFQ SIC becomes an add-on component that does notrequire modifications to the other electronics.

[0032]FIG. 4 shows a block diagram for a second embodiment architectureof the RSFQ SIC implementation operating in accordance with theinvention. Similarly, with RSFQ SIC operates with data coming after theRake combiner 410 thereby requiring no other modifications to theelectronics. The RSFQ SIC has three inputs: vector of despreadedsymbols, vector of channels gains, and vector of relative channelsdelays available at the Channel Estimation Unit 300 (CEU). The basestation receiver described supports coherent reception of the channel, afront-end digitizer with minimum required resolution 5 bits and 4 timesoversampling over the chip rate 3.84 mcps, amplitude estimation for thechannels, and the relative delay estimation for the channels.

[0033] With regard to the data channel, due to the successive method ofinterference rejection there is a trade off between system load, datarate and number of iterations required to reach target performance. Theload of the system is defined as a product of number of active DedicatedPhysical Channel (DPCH) and number of effective multi-paths as describedearlier. Supporting SNR gain in 10 dB at BER equal to 0.001, the RSFQSIC processes at least 100 DPCH with 10 effective multi-paths atpre-spreading data rate 2 mbps. In the data channel, the data is spreadwith variable spreading factor and the channel data may be scrambled byeither long or short scrambling code based on families of the Goldcodes, m-sequences, or their combinational fellows.

[0034] Regarding the multi-path propagation conditions considered foroperation of the RSFQ SIC are multi-path fading and an Additive WhiteGaussian Noise (AWGN) environment having a maximum relative delay 0.25μs. Moreover, slow fading conditions and constant multi-path powerduring one symbol interval is assumed. Also assumed is a Rayleighamplitude distribution and equal probable relative phase shift, thatgives Gaussian power distribution with zero mean.

[0035] System Model and Detection Algorithms

[0036] Operation of the RSFQ SIC is based on block-wise interferencecancellation using the Gauss-Seidel iterative method. Upon eachiteration a vector of symbol estimates at time T is updated using theobtained on the previous iteration information about the vector ofsymbols transmitted at time T−1 and T+1. Number of iterationscorresponds to the number of time symbol intervals involved intoconsideration. The RSFQ SIC of the invention supports up to 7iterations.

[0037] The standard vector model used in the two embodimentarchitectures of the multi-user detectors of the present invention isdescribed. For the interested reader the details of which can be foundin the appendix at the end of the description.

[0038] At the conventional part of the receiver, the incoming radiofrequency (RF) signal is amplified, down-converted to baseband (complexsignals), and filtered by a chip-matched filter. The output of thechip-matched filter is digitized at the rate of Ns ƒ_(chip) samples persecond, where Ns is the oversampling factor and ƒ_(chip) is the chiprate (ƒ_(chip)=3.84 Mchip s⁻¹ in W-CDMA). The digital signal isforwarded to the matched filter, where each of the K users are separatedusing matched filters tuned to the spreading codes of each individualuser. If the channel is frequency selective (several time-shiftedreplicas are received), we need one matched filter per user and signalpath. The signal paths corresponding to one user are then weightedtogether in the Rake receiver.

[0039] Since matched filter outputs are already available in existingsystems, it is convenient to allow a multi-user detector to use theseoutputs. Hence, the multi-user detector becomes an add-on that can beadded later if improved performance is needed. Furthermore, it can beshown that the matched filter bank is actually an optimum pre-processingdevice in the sense that all necessary information needed to performoptimum multi-user detection is available.

[0040] The output of the bank of matched filters can be expressed invector form as (see appendix),

y _(MF) =R _(MF) Hd+n   (1)

[0041] where y_(MF) is the output of a bank of matched filters, R_(MF)is a real-valued cross-correlation matrix, H is a block diagonal matrixof complex channel gains, d is a vector of the bits of all users (codedas ±1), and n is a complex-valued noise vector.

[0042] The task of the receiver is to detect d given the observation y.This can be done in many ways. We will here describe two possiblevariants of the so-called decorrelator multi-user detector. The twoembodiment architectures are also illustrated in FIGS. 3 and 4. Theiterative methods that approximate the decorrelating detector aredescribed in the section on Successive Interference Cancellation.

[0043] Decorrelator-Rake Receiver

[0044] Regarding to the first embodiment architecture, we note that,

y _(MF) =R _(MF) (Hd+R _(MF) ⁻¹ n)=R _(MF) u _(MF).

[0045] Hence, we can find u_(MF) by solving a system of linearequations. This referred to as the decorrelating step, since it isequivalent to pre-multiplying with the inverse of a correlation matrix,

u _(MF) =R _(MF) ⁻¹ y _(MF) =Hd+R _(MF) ⁻¹ n.   (2)

[0046] We can form the decision for d by Rake-combining the output fromthe decorrelator as,

{circumflex over (d)}=sgn Re{H*u _(MF)}=sgn Re{H*Hd+H*R _(MF) ⁻¹n.}  (3)

[0047] where the superscript * indicates the complex conjugate transposeand the sign operation is taken element-wise. Since H*H can be shown tobe a diagonal matrix with positive elements, we make correct decisionsif the noise vector is sufficiently small. The resulting architecture isshown in FIG. 3.

[0048] The elements of the H matrix are produced by the channelestimation unit. However, the RSFQ part of the receiver needs to computeR_(MF). In the appendix, it is shown that the (q, r) the element ofR_(MF) can be computed to be, $\begin{matrix}{\left\lbrack R_{MF} \right\rbrack_{q,r} = {\sum\limits_{m = {- {({N - 1})}}}^{N - 1}{v\left( \left\lbrack {{mN}_{s} + {\left( {n - n^{\prime}} \right){NN}_{S}} + \left( {p_{k,l} - P_{k^{\prime},l^{\prime}}} \right) +} \right. \right.}}} \\{\left. {\left. {QN}_{s} \right\rbrack {T_{c}/N_{s}}} \right){\sum\limits_{i = 0}^{N - 1}{{c_{n,k}^{\prime}\lbrack i\rbrack}{c_{n^{\prime},k^{\prime}}^{\prime}\left\lbrack {i - m} \right\rbrack}}}}\end{matrix}$

[0049] where k, 1, n and k′, l′, n′ depend on q and r, c_(n,k) [i] isthe code sequence for the nth user's kth symbol, v(t) is a causal pulsewith a raised-cosine spectrum (assumed to have effective support [0,2QT_(c)]), T_(c)=1/ƒ_(chip), and τ_(k,l)=p_(k,l)T_(c)/N_(s) is the delayof the kth user's lth propagation path. The samples of v(t) can bepre-computed and stored in a table, and the code sequences are generatedin the receiver front-end. We note that [R_(MF)]_(q,r) is a linearcombination of 2N−1 terms, where each term is a cross-correlationbetween the binary code sequences c_(n,k) ^(′)[i] and c_(n′,k′)[i] at acertain lag.

[0050] Rake-Decorrelator Receiver

[0051] With regard to the second architecture embodiment, we can alsostart with Rake-combining as, $\begin{matrix}\begin{matrix}{y_{rake} = {H^{*}y_{MF}}} \\{= {{H^{*}R_{MF}{Hd}} + {H^{*}n}}} \\{= {{R_{rake}d} + {H^{*}n}}} \\{= {R_{rake}\left( {d + {R_{rake}^{- 1}H^{*}n}} \right)}} \\{= {R_{rake}u_{rake}}}\end{matrix} & (4)\end{matrix}$

[0052] followed by a decorrelating step,

u _(rake) =R _(rake) ⁻¹ y _(rake) =d+R _(rake) ⁻¹ H*n.   (5)

[0053] Again, we will make correct decisions if the noise vector issufficiently small. The resulting architecture is shown in FIG. 4.

[0054] Clearly, the receiver needs access to R_(rake). In the appendix,it is shown that the (q, r)th element of R_(rake) can be computed to be,$\begin{matrix}{\left\lbrack R_{rake} \right\rbrack_{q,r} = {\sum\limits_{i = 1}^{L}{\sum\limits_{i^{\prime} = 1}^{L}{h_{k,l}^{*}{h_{k^{\prime},l^{\prime}}^{*}\lbrack A\rbrack}_{l,l^{\prime}}}}}} & (6)\end{matrix}$

[0055] where A is a certain L×L submatrix of R_(MF), and h_(k,l) is thecomplex channel gain of the kth user's lth propagation path. Thedefinitions of A, k and k′ depend on q and r.

[0056] Successive Interference Cancellation

[0057] In the previous section we have presented two architectures formulti-user detection; the decorrelator-Rake and the Rake-decorrelatorillustrated in FIGS. 3 and 4 respectively. As stated previously, thedecorrelator solves a system of linear equations (equations (2) or (5)).These linear systems can be solved using iterative methods as, forexample, described in publication [4] by Rasmussen L K, Lim T J andJohansson A-L 2000, “A Matrix-Algebraic Approach To SuccessiveInterference Cancellation”, CDMA IEEE Trans. Commun. 48 145-51. The twomain categories are the SIC (Gauss-Seidel algorithm) and the ParallelInterference Canceller (PIC) (Jacobi algorithm). The SIC is, in general,more reliable in the sense that it is guaranteed to converge with anyinitial vector (if the cross-correlation matrix is positive definite,which is a reasonable assumption). Another advantage of the SIC is thefast rate of convergence. It has been shown that somewhere around seveniterations of SIC are sufficient to reach convergence. Afterconvergence, the resulting bit error rate is the same as for thedecorrelating receiver described previously.

[0058] To perform iterations on a symbol level two basic hardware blocksare required: the partial cross-correlation unit 320 (PCU) and theiterative linear system solver (330, 340) (ILSS). The PCU 320 calculatesthe elements in the cross-correlation matrices R_(MF) or R_(rake). Theoutputs from the PCU 320 are used by the ILSS (330, 340) to estimateeither Hd or d depending on the architecture used. In the case of thedecorrelator-Rake (FIG. 3) the estimates of Hd are Rake-combined to form{circumflex over (d)} as described in equation (3). Since thecross-correlation matrix is banded, the Gauss-Seidel algorithm can bemodified to be a block-wise iteration on sub-blocks of the matrix. Thewidth of the band is 3 KL for R_(MF) and 3K for R_(rake).

[0059] The computation and hardware complexity of the SIC depends highlyon the system parameters. In accordance with the embodiment, a targetrealization of the SIC that takes into account realistic parameters ofthe commercially operational W-CDMA systems of 100 simultaneous voiceuser per sector i.e. K=100, at least 10 resolvable multipath componentsi.e. L=10, and the effective support of the chip waveform determined byQ=3. Under these considerations the throughputs of the PCUs for thedecorrelator-Rake and Rake-decorrelator (FIG. 4) receivers are 90 Gelements per second and 9 M elements per second, respectively, withcomputational complexity scaling as O(N(KL)²) and O(N(KL)²)+O((KL)²),and falling in the Teraflop range. The throughputs of the ILSS units arethen 30 Msymbol/s and 3 Msymbol/s for the same receivers withcomputational complexity scaling as O((KL)²) and 0(K²) respectively.

[0060] RSFQ Implementation Issues

[0061] Due to the Teraflop range of operations, the SIC would requireapplication specific integrated circuits (ASICs) for implementation ofboth parts, computing the elements of the cross-correlation matrix andperforming iterations.

[0062] The computation of each element of the cross-correlation matricesR_(MF) and R_(rake) consists of four parts:

[0063] generating all K L PN codes corresponding to all channel delaysplus (2Q+1)KL replicas shifted on ±QT_(c);

[0064] correlating all codes with each other;

[0065] weighting the correlations with the chip waveform values v[q] toform R_(MF);

[0066] and further combining them using channel coefficients h_(k,l) toform R_(rake).

[0067] The following is a discussion of the architectures of the PCUbuilding block, parallel-serial RSFQ cross-correlation combiner (CC),the operation of the ILSS building block, and the parallel-serial RSFQmultiply-accumulate unit (MAC).

[0068] Cross-Correlation Combiner

[0069]FIG. 5 shows a block diagram of a two-channel RSFQcross-correlation combiner operating in accordance with the invention.The CC calculates each element of R_(MF) through three steps: generatingthe codes; computing cross-correlations; and computing their linearcombinations using chip waveform values. The CC block has a regularstructure and, for simplicity, two channels will be considered. Two25-bit PN generators (500, 502) produce a maximum of 128 bit userspecific codes c_(k,l) ¹, c_(k′,l′) ², using, as input from the channelestimation unit (CEU), two 25 bit delay masks m_(k,l) ¹ and m_(k′,l′)²+Q, representing symbol and chip offsets. The values of c_(k′,l′) ²[q]are taken from the bits of the second PN generator shifted from the lastbit on 2Q.

[0070] The correlation measure of (c¹, c²[q]) is calculated as thenumber of equal elements minus the number of unequal elements in the twosequences. Therefore, each pair of output bits from the PN generators iscompared on XOR and AND gates 510, then the results are forwarded to theripple counter 520. The counter requires 8 bits to be able to processthe maximum 128 is and to represent the result in two-th complementaryform.

[0071] To achieve partial cross-correlations, an internal clockgenerator is used that produces the number of clocks determined by therelative delays between codes. All partial cross-correlations aremultiplied on constant supplied from CEU chip waveform gains v[q] (5 bitinteger numbers) with the use of 2Q serial multipliers and accumulatedwith the help of 13 bit adders, one for each counter.

[0072] The code generation, computing cross-correlations, multiplicationand accumulation operations are pipelined. The critical stage of thepipeline is the generation and comparison of the maximum 128 bit codesthat should be partially parallelized to achieve the required speed of4.5 GHz. The serial PN generator based on the linear feedback shiftregister (LFSR) can be replaced by the sixteen identical generators.Each of these produces 8 bits of the 128 bit sequence. After 8 bits, thecontents of the shift register are loaded into the next generator, andthe previous one can be used for generation of the new code. The RSFQLFSR PN generator has a four-clock initialization cycle that makes thelatency of the whole construction with sixteen generators equal totwelve clocks. The interested reader may refer to the publication [11]by present inventor Kidiyarova-Shevchenko A. Yu 2002 RSFQ spreading codegenerator for multiuser detection Physica C, v. 368, pp. 222-226. Theoperation of the counters is parallelized in the same way, by replacingone 7 bit counter on 16 with the number of bits increasing from 3 to 7.When the first 8 bits have been processed in the first counter, thecontents are moved to the next.

[0073] In total, all the PN generators in the PCU consume about 22×10³Josephson junctions, with 340 Josephson junctions per generator (seeabove publication). The implementation of comparators and counters wouldrequire 17×10³ Josephson junctions. Each integer 5×7 multiplier andadder consists of about 400 Josephson junctions, which leads to 24×10³Josephson junctions in the PCU. Further discussion of which is given inthe publication [12] by Kidiyarova-Shevchenko A Yu 2002 RSFQ iterativelinear system solver for superconducting multiuser detection Physica C,v. 372-376, pp. 131-134. Targeting the integration density of 10,000Josephson junctions per chip, the implementation of the PCU would bepossible with a multichip-module (MCM) of about 63 chips. Such an amountof 5×5 mm² chips can be collected from one 6 inch wafer with a yield of20%.

[0074] ILSS Building Blocks

[0075]FIG. 6 shows a simplified block diagram of an exemplary RFSQ MAC(multiply accumulate unit) operating in accordance with the invention.As mentioned earlier, the complexity of the ILSS is determined bycomplex MACs performing the multiply/accumulate operation over thecross-correlation matrix row and element of the complex vector of thedata bit estimates. Since the real and complex components of the signalcan be processed independently, one complex MAC is a combination of twoidentical integer MACs. Each MAC consists of a shift register 610 tostore the elements of vector y_(MF), a serial multiplier 620 to performthe multiplication of vector y on matrix R_(MF) row, and a paralleladder 630 that accumulates the results of the multiplication andcontents of the shift register. In accordance with the Gauss-Seidelalgorithm, each MAC uses elements of the vector y_(MF) recently updatedfrom the previous stages. Each MAC operates simultaneously and the unitscommunicate through a single bit bus.

[0076] The latency of the stage is the sum of the synchronization timeof three clocks and the delay of the serial multiplier, which in theconventional case is N+2, where N≦16 is the processing word length. Afurther discussion is given in the publication by Kidiyarova A. Yu. Etal RSFQ Asynchronous Serial Multiplier and Spreading Codes Generator forMultiuser Detector, IEEE Trans. Appl. Supercond. 2003 in press. The RSFQserial multiplier can be improved with the help of the ‘shifting overzeros’ technique that allows us to compute N×N bit products over−0.7(N+2) clock cycles and to use 30×N=390 Josephson junctions. Theexecution time of such a multiplier becomes data-dependent and requiresthe asynchronous operation of the ILSS. However, the asynchronousoperation is also natural for the implementation of the PCU where theamount of computations is dependent on overlapping between symbols.

[0077] Each stage of the ILSS unit processes both the complex and realcomponents of the data and comprises approximately 3000 Josephsonjunctions. In total, the realization of the ILSS would require a MCMwith about 20 chips operated at 54 GHz and with an integration densityof 10,000 Josephson junctions per chip. It should be noted that thespecifications provided are only approximate in which the specificationswill vary with the evolutionary state of the technology.

[0078] Architecture

[0079]FIG. 7 is a general block diagram of the RSFQ SIC multiprocessorarchitecture operating in accordance with the invention. The RSFQ SIC isa vector machine performing iterative solution of system of linearequations built on chains of processors consisting of an RSFQMultiply-accumulate unit (MAC) and an RSFQ Partial crosscorrelation unit(PCU). All processors 710 in a chain operate in parallel such that eachblock is computing one dedicated element of vector of transmittedsymbols at time T. In one processor, a PCU unit is used for generationof the interference coefficients corresponding to this element and a MACunit is used for interference cancellation at this element. Between theprocessors are dynamic memories, implemented as RSFQ shift registers720. The dynamic memory is used for loading the initial data from thedespreader or Rake combiner to store the intermediate estimates of thereceived symbols, in addition to communicating data between iterativestages.

[0080] Each MAC calls for its interference coefficients from theattached PCU stage. The PCU stage computes cross-correlationcoefficients between codes using delay masks obtained from the CEU,combines these coefficients against the waveform amplitudes and combinesthe result against the channel amplitudes. MAC stages produce symbolestimates, one by one. Each obtained symbol estimate is fed to the nextprocessors until the end of iteration. The initial vector for the firstchain at the beginning of each computational cycle is the output of thedespreader of the Rake combiner.

[0081] Performance

[0082] Due to the successive nature of the algorithm the RSFQ SICintroduces delay equal to the 3NTproc, where N is the dimension of thesystem and Tproc, is a time required for execution of one PCU and MACoperation. Therefore, the throughput of the SIC is ƒ_(data)=ƒ_(proc)/3N.In the decorrelator-Rake circuit both devices have similar delays comingfrom the serial spreading code generator and the serial multiplier. Inthe Rake-decorrelator circuit PCU introduces the major delay sinceadditional serial multipliers are used for combining the multi-pathsagainst their amplitudes. As a result both approaches are comparable interms of throughput and hardware complexity.

[0083] Each processor operates with fixed-point arithmetic with maximum16 bits precision. RSFQ implementation of PCU and MAC is based onparallel-serial architecture. Overall complexity will be dependent oncurrently available and future fabrication technology.

[0084] The present invention contemplates a general approach to theapplication of fast RSFQ technology into multi-user detection in 3G,W-CDMA, cellular systems. The utilization of fast speed of RSFQ circuitsin order to make multi-user detection simple and at the same timereliable, which is not practical with convention silicon-basedtechnologies. The speed advantage of RSFQ allows computations in realtime of all the elements of the cross-correlation matrix between codesto realize the SIC, which gives better performance and requires lesshardware than parallel algorithms. In the realization of RSFQ, afull-blown wideband CDMA system was considered with 100 voice equivalentchannels where the parameters of which, have not fully been consideredfor multi-user detection. Given the present state of RSFQ technology,implementation of the SIC is estimated to require a MCM with about 80chips at an integration density of 10,000 Josephson junctions per chipand an operating frequency of approximately 54 GHz. As the technologyadvances, the operational speeds will increase accompanied withdrastically reducing the number of chips thereby further lowering thecost of implementing the SIC. The resulting reduction in interferenceprovided by the invention is especially suitable for transmitting datasince the wireless transmission of data is much more sensitive to biterrors when compared to voice transmissions.

[0085] Possible Modifications to Embodiments

[0086] A first possible modification can be that inside of mainiterations dealing with signals arrived at different time intervals, theso-called outer iterations, the inner iterations can be done under someconditions in parallel way. This is possible because the overallperformance of the RSFQ SIC is not strictly dependent on how the inneriterations are performed and thus a parallel algorithm such as a Jacobialgorithm can be used. The change in the algorithm would require achange in the combinations of processors and a more sophisticatedmechanism in managing bus cash.

[0087] A second possible modification is of a more general nature inthat the signal processing required for multi-user detection canpotentially be moved before the matched filters. In this case, the bitstream from the front-end ADC is fed directly into the RSFQ SIC. TheRSFQ SIC has KL blocks where each block comprises a matched filter fordispreading the signal, and a decision block and modulator to encode thedetected signals back. After the encoding, the detected signals aresubtracted from the total received signal. There are algorithms thatdeal with such a structure. From the practical point of view, a largeamount of memory is needed because the incoming signal from thefront-end signal should be delayed by the time of execution of oneoperation by RSFQ SIC stage. The memory size would increase inproportion to the total number of transmitted signals per packetmultiplied by the oversampling factor. Various types of memory can beused such as static or dynamic memories (SRAM or DRAM), or flash memory(non-volatile memory). In the case of static memory, a single block isplaced before all stages of RSFQ SIC. The introduction of static memoryincreases the hardware complexity and reduces the speed due to theaddressing, however advances in technology may mitigate this aspectsomewhat. In the case of dynamic memory, the RSFQ shift registers ofappropriate length are placed between each of the stages.

[0088] Furthermore, the RSFQ SIC architecture can be based on thefull-parallel arithmetic, e.g. full-parallel multipliers, adders,registers, and code generators. Full parallel architectures aredifficult to implement outside laboratory conditions due to the limitedintegration density in the fabrication processes for reliable RSFQdevices. However, as technology advances and densities increase thiswill be much more feasible parallel architectures are possible. Ingeneral, to realize full-parallel architecture an integration density ofabout 100000 Josephson junctions per chip is required.

[0089] Although the invention has been described in some respects withreference to specified embodiments thereof, variations and modificationswill become apparent to those skilled in the art. In particular, theinvention is applicable to all direct sequence CDMA systems such asCDMA2000, for example, where the code correlator block is readilyadaptable to different standards and leaving the other blockssubstantially unchanged. Furthermore, the invention can be used, forexample, for interference rejection in a ship-to-land (and vice versa)communications link and in military communication systems. It istherefore the intention that the following claims not be given arestrictive interpretation but should be viewed to encompass variationsand modifications that are derived from the inventive subject matterdisclosed.

[0090] Appendix

[0091] Vector Model

[0092] Regarding the iterative methods for solving systems of linearequations in the invention includes the need to form the R_(MF) orR_(rake) matrices, in which we show that this depends on the chipwaveform and the codes of the user, channel gains and delays.

[0093] For clarity, a simplified model of a full-blown W-CDMA system isdiscussed. However, the described architecture of the multi-userdetector can be easily adapted for a more complicated scenario.Specifically, a K-user system is considered where the baseband signalfor each kth user is,${s_{k}(t)} = {\sum\limits_{n = 0}^{P - 1}{{d_{k}\lbrack n\rbrack}{c_{n,k}^{\prime}(t)}}}$

[0094] where P is the number of transmitted bits per user (packetlength) and,${c_{n,k}^{\prime}(t)} = {\sum\limits_{j = 0}^{N - 1}{{c_{n,k}^{\prime}\lbrack j\rbrack}{{\psi \left( {t - {nT} - {jTc}} \right)}.}}}$

[0095] The signal ψ(t) is the chip waveform, ƒ_(data)=1/T is the datarate (bits per second), and ƒ_(clip)=1/T_(c)=N/T_(c) is the chip rate.

[0096] We assume that all the user signals are transmitted over L-pathslowly fading channels. Hence, the noise-free received baseband signalcan be written as,${z(t)} = {\sum\limits_{k = 1}^{K}{\sum\limits_{l = 1}^{L}{h_{k,l}{s_{k}\left( {t - \tau_{k,l}} \right)}}}}$

[0097] where h_(k,l) and τ_(k,l) are the complex channel gain and delayof the kth user's lth path, respectively.

[0098] The received signal is fed through a linear filter with animpulse response ψ(−t) (i.e. chip-matched filtering), and the output ofthe filter is sampled every T_(c)/N_(s) seconds (i.e. oversampling witha factor N_(s)) yielding the discrete time signal,${r\left( {{iT}_{c}/N_{s}} \right)} = {{{{z(t)}^{*}{\psi \left( {- t} \right)}}_{t = {{iTc}/{Ns}}}} = {\sum\limits_{k = 1}^{K}{\sum\limits_{l = 1}^{L}{h_{k,l}{\sum\limits_{n = 0}^{P - 1}{{d_{k}\lbrack n\rbrack}{c_{n,k,l}\lbrack i\rbrack}}}}}}}$

[0099] where * is the convolution operator. The effective spreading codeis,${c_{n,k,j}\lbrack i\rbrack} = {\sum\limits_{j = 0}^{N - 1}{{c_{n,k}^{\prime}\lbrack j\rbrack}{v\left( {\left\lbrack {i - {jN}_{s} - {nNN}_{s} - p_{k,l}} \right\rbrack {T_{c}/N_{s}}} \right)}}}$

[0100] where v(t)=ψ(t) * ψ(−t) and τ_(k,l)=p_(k,l)T_(c)/N_(s). We assumethat v(t) is a pulse with a raised-cosine spectrum with roll-off a=0.22(used in W-CDMA) with the effective support t ∈ [0, 2QT_(c)].

[0101] We form the vector r,

r=[r(0) r(T _(c) /N _(s)) . . . r((M−1)T _(c) /N _(s))]^(T)

[0102] where M is chosen to be large enough to capture the contributionfrom all bits of all users. It is easy to show that,

r=CHd

[0103] where the columns of the M×KLP matrix C are samples ofc_(n,k,l)[i]. To be precise, the (i, l+(k−1)K+nLK)th element of C isequal to c_(n,k,l)[i−1]. The KLP×KP matrix H is block-diagonal anddefined as,

H=diag(H ₀ , H ₁ , . . . , H _(P−1))

H _(n)=diag(h ₁ , h ₂ , . . . , h _(K))

h _(k) =[h _(k,l) h _(k,2) . . . h _(k,L)]^(T).

[0104] Finally,

d=[d ₁[0] . . . d _(K)[0] . . . d ₁[1] . . . d _(K)[1] . . . d ₁ [P−1] d_(K) [P−1]]^(T)

[0105] The output from the bank of the KL matched filters (one filterper user and path) can be found to be,

y_(MF)=C_(MF) ^(T)r

[0106] where C_(MF) is of the same dimensions as C. The (i,l+(k−1)K+nLK)th element of C_(MF) is equal to C_(MF,n,k,l)[i−1], whichis defined as,${C_{{MF},n,k,l}\left\lbrack {{iN}_{s} + {nNN}_{s} + p_{k,l} + {QN}_{s}} \right\rbrack} = \left\{ \begin{matrix}{{c_{n,k}^{\prime}\lbrack i\rbrack},} & {{{{{for}\quad i} = 0},1,\ldots \quad,{N - 1}}} \\{0,} & {{otherwise}}\end{matrix} \right.$

[0107] Hence, we have shown that,

R_(MF)=C_(MF) ^(T)C

[0108] It can also be shown that, due to the finite support of v(t), thematrix C_(MF) is a band matrix with bandwidth 2KL. The (q, r)th elementof R_(MF) can be computed to be, $\begin{matrix}{\left\lbrack R_{MF} \right\rbrack_{q,r} = {\sum\limits_{i = 0}^{N - 1}{{c_{{MF},n,k,l}\left\lbrack {{iN}_{s} + {nNN}_{s} + {QN}_{s} + p_{k,l}} \right\rbrack} \times}}} \\{{c_{n^{\prime},k^{\prime},l^{\prime}}\left\lbrack {{iN}_{s} + {nNN}_{s} + {QN}_{s} + p_{k,l}} \right\rbrack}} \\{= {\sum\limits_{i = 0}^{N - 1}{{c_{n,k}^{\prime}\lbrack i\rbrack}{\sum\limits_{j = 0}^{N - 1}{{c_{n^{\prime},k^{\prime}}^{\prime}\lbrack j\rbrack}{v\left( \left\lbrack {{\left( {i - j} \right)N_{s}} + {\left( {n - n^{\prime}} \right){NN}_{s}} +} \right. \right.}}}}}} \\\left. {\left. {\left( {p_{k,l} - p_{k^{\prime},l^{\prime}}} \right) + {QN}_{s}} \right\rbrack {T_{c}/N_{s}}} \right) \\{= {\sum\limits_{m = {- {({N - 1})}}}^{N - 1}{v\left( \left\lbrack {{mN}_{s} + {\left( {n - n^{\prime}} \right){NN}_{s}} + \left( {p_{k,l} - p_{k^{\prime},l^{\prime}}} \right) +} \right. \right.}}} \\{\left. {\left. {QN}_{s} \right\rbrack {T_{c}/N_{s}}} \right){\sum\limits_{i = 0}^{N - 1}{{c_{n,k}^{\prime}\lbrack i\rbrack}{c_{n^{\prime},k^{\prime}}^{\prime}\left\lbrack {i - m} \right\rbrack}}}}\end{matrix}$

[0109] where k, l, n and k′, l′, n′ depend on q and r. Furthermore, anelement of R_(rake) is computed to be,

[R_(rake)]_(q,r) =h _(k) ^(*) Ah_(k′)

[0110] where A is a certain L×L submatrix of R_(MF). Again, thedefinitions of A, k and k′ depend on q and r. We can rewrite theexpression for [R_(rake)]_(q,r)$\left\lbrack R_{rake} \right\rbrack_{q,r} = {\sum\limits_{l = 1}^{L}\quad {\sum\limits_{l^{\prime} = 1}^{L}\quad {h_{k,l}^{*}{h_{k^{\prime},l^{\prime}}^{*}\lbrack A\rbrack}_{l,l^{\prime}}}}}$

1. A method of reducing multi-user interference (MAI) in a wirelessdirect sequence CDMA wireless system comprising at least one basestation transmitting to and receiving signals from a plurality of mobilestation users, wherein the method includes reducing interference with amulti-user detector located within said at least one base stationcomprising the steps of: receiving at the base station a digital inputsignal comprised of a plurality of binary code sequences from the mobilestation users; processing the digital input signal with a bank ofmatched filters and producing an output vector from the matched filterbank. feeding the output vector into a superconducting rapid single fluxquantum (RSFQ) vector processing unit; applying a successiveinterference cancellation (SIC) iterative technique to solve across-correlation matrix to decorrelate the user binary code sequences;reducing interference by removing interference components between thebinary code sequences; and feeding the output from the RSFQ SIC into abank of Rake combiners to recover original data transmitted by themobile users.
 2. A method according to claim 1 wherein the implementedmulti-user detector of the direct sequence CDMA system operates inaccordance with the Wideband Code Division Multiple Access (W-CDMA)standard.
 3. A method according to claim 1 wherein in the receivingstep, the binary code sequences from the mobile station users are W-CDMAGold long spreading codes having a sequence length of 2⁴¹, or VL-Kasamishort spreading codes with a sequence length of
 256. 4. A methodaccording to claim 1 wherein the RSFQ and SIC represents a vectorprocessing unit operating at a frequency of at least 54 GHz and iscapable of removing interference in a W-CDMA system operating with atleast 100 simultaneous voice user per sector with at least 10 resolvablemultipath components per user, and the effective support of the chipwaveform determined by Q=3.
 5. A method according to claim 1 wherein inthe applying step, the SIC employs a Gauss-Seidel iteration algorithm tosolve the cross-correlation matrix of binary code sequences given by,$\left\lbrack R_{MF} \right\rbrack_{q,r} = {\sum\limits_{m = {- {({N - 1})}}}^{N - 1}\quad {{v\left( {\left\lbrack {{mN}_{s} + {\left( {n - n^{\prime}} \right){NN}_{S}} + \left( {p_{k,l} - P_{k^{\prime},l^{\prime}}} \right) + {QN}_{s}} \right\rbrack {T_{c}/N_{s}}} \right)}{\sum\limits_{i = 0}^{N - 1}\quad {{c_{n,k}^{\prime}\lbrack i\rbrack}{c_{n^{\prime},k^{\prime}}^{\prime}\left\lbrack {i - m} \right\rbrack}}}}}$

where k, l, n and k′, l′, n′ depend on q and r, c_(n,k) ^(′) [i] is thecode sequence for the kth user's nth symbol, v(t) is a causal pulse witha raised-cosine spectrum, T_(c)=1/ƒ_(chip), andτ_(k,l)=p_(k,l)T_(c)/N_(s) is the delay of the kth user's lthpropagation path.
 6. A method according to claim 1 wherein a partialcross-correlation unit (PCU) is used to calculate the elements in thecross-correlation matrix R_(MF).
 7. A method according to claim 6wherein the output from the PCU is used by an iterative linear systemsolver (ILSS) to estimate a block diagonal matrix Hd whereby theGauss-Seidel algorithm is modified to be a block-wise iteration onsub-blocks of the matrix.
 8. A method according to claim 1 wherein theRSFQ processing unit receives input that includes a vector of channelsgains and vector of relative channels delays from a channel estimationunit (CEU).
 9. A method of reducing multi-user interference (MAI) in awireless direct sequence CDMA wireless system comprising at least onebase station transmitting to and receiving signals from a plurality ofmobile station users wherein the method includes reducing interferencewith a multi-user detector located within said at least one base stationcomprising the steps of: receiving at the base station a digital inputsignal comprised of a plurality of binary code sequences from the mobilestation users; processing the digital input signal with a bank ofmatched filters to produce a matched filter output vector; receiving thematched filter output vector into a bank of Rake combiners to produce aRake combiner output vector; receiving the Rake combiner output vectorinto a superconducting rapid single flux quantum (RSFQ) vectorprocessing unit; applying a successive interference cancellation (SIC)iterative technique to solve a cross-correlation matrix to decorrelatethe user binary code sequences; and reducing the interference byremoving interference components between the binary code sequences. 10.A method according to claim 9 wherein the multi-user detector method ofthe direct sequence CDMA system operates in accordance with the WidebandCode Division Multiple Access (W-CDMA) standard.
 11. A method accordingto claim 9 wherein, in the receiving step of the digital input signal,the binary code sequences from the mobile station users are W-CDMA Goldlong spreading codes having a sequence length of 2⁴¹, or VL-Kasami shortspreading codes with a sequence length of
 256. 12. A method according toclaim 9 wherein the RSFQ and SIC represents a vector processing unitoperating at a frequency of at least 54 GHz and is capable of removinginterference in a W-CDMA system operating with at least 100 simultaneousvoice user per sector with at least 10 resolvable multipath componentsper user, and the effective support of the chip waveform determined byQ=3.
 13. A method according to claim 9 wherein in the applying step, theSIC employs a Gauss-Seidel iteration algorithm to solve thecross-correlation matrix of binary code sequences given by,$\left\lbrack R_{rake} \right\rbrack_{q,r} = {\sum\limits_{i = 1}^{L}\quad {\sum\limits_{i^{\prime} = 1}^{L}\quad {h_{k,l}^{*}{h_{k^{\prime},l^{\prime}}^{*}\lbrack A\rbrack}_{l,l^{\prime}}}}}$

where A is a certain L×L submatrix of R_(MF), and h_(k,l) is the complexchannel gain of the kth user's lth propagation path.
 14. A methodaccording to claim 1 wherein a partial cross-correlation unit (PCU) isused to calculate the elements in the cross-correlation matrix R_(rake).15. A method according to claim 14 wherein the output from the PCU isused by an iterative linear system solver (ILSS) to estimate a blockdiagonal matrix d whereby the Gauss-Seidel algorithm is modified to be ablock-wise iteration on sub-blocks of the matrix.
 16. A method accordingto claim 9 wherein the RSFQ processing unit receives input that includesa vector of channels gains and vector of relative channels delays from achannel estimation unit (CEU).
 17. A method according to claim 9 whereinin the RSFQ SIC the inner iterations, performed inside the mainiterations dealing with signals arrived at different time intervals, areperformed either in a parallel way or by using a successive method. 18.A method according to claim 9 wherein in the signal processing requiredfor multi-user detection is performed on the chip level.
 19. Amulti-user detector apparatus for use with interference cancellation ina radio base station receiver operating within a direct sequence CDMAwireless system, wherein said base station includes a channel estimationunit (CEU) for estimating characteristics of a channel, a pseudo-numbergenerator for use with despreading user code sequences, a bank ofmatched filters for a in a first stage in rejecting interference in aninput signal, a bank of Rake combiners for processing signal multipaths,said multi-user detector apparatus comprising: a partialcross-correlation unit (PCU) linked to the CEU for calculating theelements of a cross-correlation matrix related to the user codesequences; and a superconducting rapid single flux quantum (RSFQ) vectorprocessing unit linked to the PCU and to the bank of matched filters,wherein the output from the bank of matched filters is fed into the RSFQprocessing unit for iterative processing, and wherein the output of theRSFQ processing unit is fed into the bank of Rake combiners for removinginterference components from the original user code sequences.
 20. Anapparatus according to claim 19 wherein the RSFQ processing unit is avector machine that comprises superconducting logic circuits capable ofperforming an successive interference cancellation (SIC) iterativetechnique for decorrelating a cross-correlation matrix associated withthe user code sequences.
 21. An apparatus according to claim 20 whereinthe RSFQ SIC comprises a plurality of Multiply-accumulate units (MAC)such that all processors in one chain are performed in parallel in sucha way that each block is computing one dedicated element of vector oftransmitted symbols at time T.
 22. An apparatus according to claim 20wherein an iterative linear system solver (ILSS) for processing aGauss-Seidel algorithm in said SIC.
 23. An apparatus according to claim20 wherein the RSFQ SIC further comprises an iterative linear systemsolver (ILSS) unit for running a Gauss-Seidel algorithm.
 24. Amulti-user detector apparatus for use with interference cancellation ina radio base station receiver operating within a direct sequence CDMAwireless system, wherein said base station includes a channel estimationunit (CEU) for estimating characteristics of a channel, a pseudo-numbergenerator for use with despreading user code sequences, a bank ofmatched filters for a in a first stage in rejecting interference in aninput signal, a bank of Rake combiners for processing signal multipaths,said multi-user detector apparatus comprising: a partialcross-correlation unit (PCU) linked to the CEU for calculating theelements of a cross-correlation matrix related to the user codesequences; and a superconducting rapid single flux quantum (RSFQ) vectorprocessing unit linked to the PCU and to the bank of matched filters,wherein the output vector from the bank of matched filters is fed intothe bank of Rake combiners, and the output vector of bank of Rakecombiners is fed into the RSFQ processing unit for iterative processingfor removing interference components from the original user codesequences.
 25. An apparatus according to claim 24 wherein the RSFQprocessing unit is a vector machine that comprises superconducting logiccircuits capable of performing an successive interference cancellation(SIC) iterative technique for decorrelating a cross-correlation matrixassociated with the user code sequences.
 26. An apparatus according toclaim 25 wherein the RSFQ SIC comprises a plurality ofMultiply-accumulate units (MAC) such that all processors in one chainare performed in parallel in such a way that each block is computing onededicated element of vector of transmitted symbols at time T.
 27. Anapparatus according to claim 24 wherein an iterative linear systemsolver (ILSS) for processing a Gauss-Seidel algorithm in said SIC. 28.An apparatus according to claim 24 wherein the RSFQ SIC furthercomprises an iterative linear system solver (ILSS) unit for running aGauss-Seidel algorithm.
 29. An apparatus according to claim 24 whereinthe signal processing required for multi-user detection is locatedbefore the matched filters and a memory for storing signal processingdata is included such that the size of the memory is in proportion tothe total number of transmitted signals per packet multiplied by theoversampling factor.